Landscape Ecology

, Volume 27, Issue 10, pp 1513–1522 | Cite as

Modeling acoustic diversity using soundscape recordings and LIDAR-derived metrics of vertical forest structure in a neotropical rainforest

  • Burak K. PekinEmail author
  • Jinha Jung
  • Luis J. Villanueva-Rivera
  • Bryan C. Pijanowski
  • Jorge A. Ahumada
Research Article


We determined the relationship between acoustic diversity and metrics of vertical forest structure derived from light detection and ranging (LIDAR) data in a neotropical rainforest in Costa Rica. We then used the LIDAR-derived metrics to predict acoustic diversity across the forest landscape. Sound recordings were obtained from 14 sites for six consecutive days during dusk chorus (6 pm). Acoustic diversity was calculated for each day as the total intensity across acoustic frequency bands using the Shannon index and then averaged over the 6 days at each site. A 10 m radius around each site was used to obtain several LIDAR-derived metrics describing the vertical structural attributes of the forest canopy. Multiple linear regression (MLR) with Akaike information criterion was used to determine a top-ranked model with acoustic diversity as the dependent variable and the LIDAR metrics as independent variables. Acoustic diversity was modeled for forested areas (where canopy height was >20 m) at 20 m resolution using coefficients obtained from the MLR, and a hotspot analysis was conducted on the resulting layer. Acoustic diversity was strongly correlated (R 2  = 0.75) with the LIDAR metrics suggesting that LIDAR-derived metrics can be used to determine canopy structural attributes important to vocal fauna species. The hotspot analysis revealed that the spatial distribution of these canopy structural attributes across the La Selva forest is not random. Our approach can be used to identify forest patches of potentially high acoustic diversity for conservation or management purposes.


Forest canopy strata Vertical canopy gaps Hotspot analysis La Selva biological station Anselin Local Moran’s I statistic Multiple linear regression 



The acoustic data in this publication were provided by the Tropical Ecology Assessment and Monitoring (TEAM) Network, which is a collaboration between Conservation International, the Missouri Botanical Garden, the Smithsonian Institution, and the Wildlife Conservation Society, partially funded by these institutions, the Gordon and Betty Moore Foundation, and other donors. The authors thank Johanna Hurtado, the manager of Volcan Barva TEAM site, for her contribution to the data collection efforts. The LIDAR data were collected and provided to the TEAM Network of Conservation International by Northrop–Grumman Corporation, through the Global Climate Monitoring Systems and 3001 remote sensing division. We thank Aaron Swanson from Northrop–Grumman Aerospace Systems for providing information regarding the LIDAR data collection and processing. This project was also partially funded by an NSF grant to BCP (III-XT Program), and an OTS travel scholarship and funds from the Department of Education GAANN Program to LJV.


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Copyright information

© Springer Science+Business Media Dordrecht 2012

Authors and Affiliations

  • Burak K. Pekin
    • 1
    • 4
    Email author
  • Jinha Jung
    • 2
  • Luis J. Villanueva-Rivera
    • 1
  • Bryan C. Pijanowski
    • 1
  • Jorge A. Ahumada
    • 3
  1. 1.Department of Forestry and Natural ResourcesPurdue UniversityWest LafayetteUSA
  2. 2.Institute for Environmental Science and PolicyUniversity of IllinoisChicagoUSA
  3. 3.Tropical Ecology Assessment and Monitoring Network, Science and Knowledge DivisionConservation InternationalArlingtonUSA
  4. 4.Division of Applied Plant EcologySan Diego Zoo Institute for Conservation ResearchEscondidoUSA

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